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Conference Paper
Symmetry Preprocessing Architecture for Neural Network Quantum Error Decoder
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- Authors
- Eunyoung Cho, Jin-Ho On, Chei-Yol Kim, Gyu-Il Cha
- Issue Date
- 2022-12
- Citation
- International Conference on Internet (ICONI) 2022, pp.274-276
- Language
- English
- Type
- Conference Paper
- Abstract
- Fault-tolerant quantum computing and the quantum internet require accuracy and execution speed to provide error rates similar to traditional computing. Due to the limitations of graph-based error decoding algorithms in large-scale qubit systems, machine learning has been adopted to provide approximate optimization solutions. However, it is still in a state of overcoming the limitation of the input bandwidth of the decoder. Recently, many research results show that applying the mathematical symmetry of quantum physics theory in machine learning leads to performance improvement. In this paper, we propose a symmetric preprocessing architecture, which generates, augments, and compresses symmetric information to provide more efficient error correction in the large-scale quantum system while accommodating continuously improved quantum device technologies and error correction codes.
- KSP Keywords
- Approximate optimization, Error Correction Code(ECC), Execution speed, Fault tolerant, Graph-based, Optimization solutions, Quantum device, Quantum internet, decoding algorithm, error rate, large-scale